A safe and secured humanoid interface system to control data access and method thereof

Abstract

Disclosed is a safe and secure humanoid interface system (100) for managing data access and security within artificial intelligence (AI) environments. The system (100) comprises a source (102) for data input, a user access module (104) for authentication, and a data access and encryption module (106) for encrypting and controlling access to data. An ML model (108) processes encrypted data to generate insights, while a feedback mechanism (110) gathers user feedback. Crucially, a federated learning container (112) conducts computations on encrypted data, preserving privacy. This system (100) facilitates seamless communication between its components, ensuring robust data security and enabling effective utilization of AI capabilities. The present disclosure also relates to a method (200) of secure interface system (100) for managing access to data.

Description

[0001] A SAFE AND SECURED HUMANOID INTERFACE SYSTEM TO CONTROL DATA ACCESS AND METHOD THEREOF

[0002] TECHNICAL FIELD

[0003] [1].The present disclosure relates to a field of artificial intelligence (Al) systems and data security. More particularly, the present disclosure relates to a secure interface system for managing access to data and its method thereof.

[0004] BACKGROUND

[0005] [2]. In the realm of artificial intelligence (Al), rapid advancements have propelled its integration into various aspects of daily life, from personalized recommendations to autonomous systems. However, alongside its benefits, Al also poses significant challenges and risks. One prominent concern is the potential for Al systems to produce incorrect predictions or outcomes, which can have detrimental effects in critical applications such as healthcare or finance. Moreover, Al algorithms may exhibit behaviors that are difficult for humans to understand or interpret, leading to instances where Al bots have been shut down due to incomprehensible communication patterns. Additionally, the misuse of sensitive information by Al tools presents a pressing issue, raising questions about data privacy and security in Al-driven environments.

[0006] [3]. Furthermore, the growing reliance on Al has raised concerns about its impact on human cognition and decision-making processes. There is evidence suggesting that constant exposure to Al-generated content may hinder individuals' ability to think critically and independently. In educational settings, for example, students may resort to copying content from Al systems rather than engaging in the creative process of developing their own thoughts and ideas. This trend not only undermines educational objectives but also underscores broader societal challenges related to the erosion of critical thinking skills in the Al era.

[0007] [4]. Moreover, the integration of machine learning (ML) models into various applications has introduced new complexities regarding data security and privacy. ML algorithms often rely on vast amounts of raw data to generate insights and predictions, raising concerns about the confidentiality and integrity of sensitive information. Without robust mechanisms in place to manage data access and encryption, Al-driven systems are vulnerable to security breaches and unauthorized access. Addressing these challenges require solutions that prioritize data security while fostering responsible Al usage and promoting user awareness and empowerment.

[0008] [5]. In view of the foregoing, there remains a need for technology to overcome the limitations associated with conventional data security practices.

[0009] SUMMARY

[0010] [6]. In one aspect of the present disclosure, a secure interface system for managing access to data is provided.

[0011] [7]. The system includes a source that is configured to receive one or more data.

[0012] The system further includes a user access module that is configured to authenticate user access to the system. The system further includes a data access and encryption module that is configured to encrypt data received from the source using homomorphic encryption, and to control access to the encrypted data. The system further includes an ML model that is configured to process the encrypted data and generate one or more insights. The system further includes a feedback mechanism that is configured to receive feedback on one or more insights generated by the ML model. The system further includes a federated learning container that is configured to perform computations on the encrypted data.

[0013] [8]. In some aspects of the present disclosure, the secure interface system facilitates communication between the source, user access module, data access and encryption module, ML model, feedback mechanism, and federated learning container.

[0014] [9]. In some aspects of the present disclosure, the user access module utilizes an authentication protocol to verify user identity.

[0015]

[0010] . In some aspects of the present disclosure, the data access and encryption module controls access to the encrypted data based on predefined user permissions.

[0016]

[0011] . In some aspects of the present disclosure, the feedback mechanism allows users to provide feedback on the accuracy, relevance, or bias of the insights generated by the ML model.

[0017]

[0012] . In some aspects of the present disclosure, the federated learning container enables distributed processing of the encrypted data without decrypting it.

[0013] . In a second aspect of the present disclosure, a method of secure interface system for managing access to data is provided.

[0018]

[0014] . The method includes an initial step of receiving data from a source. The method further includes a step of authenticating user access to a secure interface system. The method further includes a step of encrypting the received data using homomorphic encryption. The method further includes a step controlling access to the encrypted data based on predefined user permissions. The method further includes a step of processing the encrypted data through a federated learning container to generate insights. The method further includes a step of receiving feedback on the insights generated by the ML model, and the method includes a final step of utilizing the feedback to improve the performance of the ML model.

[0019]

[0015] . In some aspects of the present disclosure, the authentication step utilizes an authentication protocol to verify user identity.

[0020]

[0016] . In some aspects of the present disclosure, the controlling step involves granting access to specific portions of the encrypted data based on user roles.

[0021]

[0017] . In some aspects of the present disclosure, the feedback step allows users to provide qualitative or quantitative feedback on the insights.

[0022] BRIEF DESCRIPTION OF DRAWINGS

[0023]

[0018] . The above and still further features and advantages of aspects of the present disclosure become apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:

[0024]

[0019] . Figure 1 illustrates a high-level secure interface system for managing access to data, in accordance with an aspect of the present disclosure;

[0025]

[0020] . Figure 2 illustrate secure interface components of the system, in accordance with an aspect of the present disclosure;

[0026]

[0021] . Figure 3 illustrates a secure interface workflow, in accordance with an aspect of the present disclosure; and

[0027]

[0022] . Figure 4 illustrates a method of secure interface system for managing access to data, in accordance with an aspect of the present disclosure.

[0028] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029]

[0023] . The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.

[0024] . The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.

[0030]

[0025] . It is understood that when an element or layer is referred to as being

[0031] “on,” “connected to,” or “coupled to” another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0032]

[0026] . The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventor / inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies.

[0033]

[0027] . The present disclosure, therefore: provides a device and method for optimizing aquaculture pond conditions.

[0034]

[0028] . Figures 1-3 illustrate a secure interface system 100 for managing access to data, in accordance with an aspect of the present disclosure. Those skilled in the art may appreciate that Figure 1 is merely an example of a system 100 and does not constitute a limitation of system 100. The system 100 may include more or fewer components than shown in Figures 1-3, or some components may be combined, or similar components.

[0035]

[0029] . The secure interface system 100 embodies a sophisticated architecture designed to facilitate the secure and efficient management of data within artificial intelligence (Al) environments. At its core, the system 100 integrates a plurality of interconnected components, each meticulously engineered to ensure the confidentiality, integrity, and usability of data exchanged within the Al ecosystem.

[0036]

[0030] . In accordance with the embodiments of the present disclosure, the system 100 may include a source 102, which serves as the primary ingress point for data into the system 100. The source 102 may be configured to receive one or more data, facilitating seamless data integration and interoperability within the Al ecosystem. Whether sourced from sensors, databases, external applications, or other data repositories, the source 102 ensures the uninterrupted flow of data into the system 100, laying the foundation for comprehensive analysis, processing, and interpretation.

[0037]

[0031] . In some aspects of the present disclosure, the versatility of the source 102 enables the system 100 to adapt to a wide array of data types, formats, and structures, ranging from structured to unstructured, textual to multimedia, and real-time to batch data. This flexibility empowers the system 100 to effectively handle disparate data sources and formats, facilitating holistic insights and comprehensive analyses. Moreover, the source 102 is equipped with robust data ingestion capabilities, enabling it to seamlessly ingest, validate, and preprocess incoming data streams in real-time. By automating these essential data preprocessing tasks, the source 102 streamlines the data pipeline, minimizing latency, and ensuring data quality and consistency.

[0038]

[0032] . In some aspects of the present disclosure, the source 102 is architected to support scalable and resilient data ingestion workflows, capable of handling high-volume, high-velocity data streams with ease. Leveraging advanced parallel processing and distributed computing techniques, the source 102 optimizes data ingestion performance, ensuring timely and efficient processing of incoming data streams. Additionally, the source 102 incorporates fault- tolerant mechanisms and data redundancy strategies to mitigate the risk of data loss or corruption, thereby enhancing the reliability and robustness of the system 100.

[0039]

[0033] . In accordance with the embodiments of the present disclosure, the system 100 may include a user access module 104 that is meticulously engineered to fortify the security architecture of the system 100 by overseeing user authentication processes. The user access module 104 serves as the gatekeeper, regulating access to the system 100 and its associated resources with unparalleled precision and reliability. Leveraging advanced authentication protocols and mechanisms, the user access module 104 ensures that only authorized individuals or entities are granted access to the system 100, thereby safeguarding against unauthorized access and potential security breaches.

[0040]

[0034] . In some aspects of the present disclosure, the user access module 104 may include a sophisticated authentication engine that may be equipped with robust algorithms capable of verifying user credentials with utmost accuracy and efficiency. Upon initiation, the authentication engine prompts users to provide their credentials, which may include usernames, passwords, biometric data, or cryptographic keys, depending on the specified authentication requirements. Subsequently, the authentication engine meticulously scrutinizes the provided credentials, subjecting them to rigorous validation checks to ascertain their authenticity and legitimacy.

[0041]

[0035] . In some aspects of the present disclosure, the user access module 104 offers a versatile authentication framework designed to accommodate diverse authentication methods and mechanisms tailored to the unique security requirements and preferences of the system's 100 administrators. This flexibility enables the system 100 to adapt to evolving security standards and protocols, ensuring compatibility with a wide range of authentication technologies and practices.

[0042]

[0036] . In some aspects of the present disclosure, the user access module 104 implements robust access control mechanisms to enforce granular access policies based on predefined user permissions and roles. Administrators have the flexibility to define and customize access policies, specifying which users or user groups are granted access to specific resources within the system 100. This fine-grained access control empowers administrators to enforce the principle of least privilege, limiting users' access rights to only those resources essential for their designated roles and responsibilities.

[0043]

[0037] . In some aspects of the present disclosure, the user access module 104 incorporates advanced security features such as multi-factor authentication (MFA) and adaptive authentication to enhance the resilience and robustness of the authentication process. MFA requires users to provide multiple forms of authentication, such as a combination of passwords, biometric data, or one-time passcodes, thereby adding an extra layer of security against unauthorized access attempts. Similarly, adaptive authentication employs contextual information such as user location, device characteristics, and behavioral patterns to dynamically adjust the authentication requirements based on perceived risk levels, ensuring that security measures are appropriately calibrated to mitigate potential threats.

[0044]

[0038] . In some aspects of the present disclosure, the user access module 104 may utilize an authentication protocol to verify user identity.

[0045]

[0039] . In accordance with the embodiments of the present disclosure, the system 100 may include a data access and encryption module 106 that is engineered to fortify data security and confidentiality within the system 100. Central to its functionality, is the implementation of homomorphic encryption, a cutting-edge cryptographic technique renowned for its ability to perform computations directly on encrypted data without the need for decryption. Upon receiving data inputs from the source 102, the data access and encryption module 106 initiates the encryption process, leveraging homomorphic encryption algorithms to transform the raw data into an encrypted format. This encryption process ensures that sensitive information remains shielded from unauthorized access and prying eyes, mitigating the risks associated with data breaches and unauthorized disclosures.

[0046]

[0040] . In some aspects of the present disclosure, the data access and encryption module 106 may assume the critical responsibility of controlling access to the encrypted data, thereby reinforcing the system's 100 defenses against potential security vulnerabilities. By implementing granular access controls and permissions mechanisms, the module 106 meticulously manages user access to the encrypted data, ensuring that only authorized individuals or entities are granted permission to view or manipulate the protected information (3). This rigorous access control regime serves as a formidable barrier against unauthorized data access and malicious intrusions, bolstering the system's 100 overall security posture and safeguarding sensitive information from unauthorized disclosure or exploitation.

[0047]

[0041] . In some aspects of the present disclosure, the utilization of homomorphic encryption within the data access and encryption module 106 offers inherent advantages in terms of data privacy and confidentiality. Unlike traditional encryption methods, which typically require data to be decrypted before performing computations, homomorphic encryption enables computations to be carried out directly on the encrypted data, preserving its confidentiality throughout the processing pipeline. This enables users to derive valuable insights and perform analytical tasks on encrypted data without compromising its privacy or security, facilitating seamless integration with downstream analytics and machine learning processes.

[0048]

[0042] . In some aspects of the present disclosure, the data access and encryption module 106 may control access to the encrypted data based on predefined user permissions.

[0049]

[0043] . In accordance with the embodiments of the present disclosure, the system 100 may include an ML model 108 that is configured to process the encrypted data and generate one or more insights. The ML model 108 is configured with advanced algorithms and computational frameworks, and operates as the cognitive engine of the system 100, endowed with the capability to decipher complex patterns, trends, and correlations obscured within the encrypted data. Leveraging sophisticated machine learning techniques such as deep learning, neural networks, and statistical modeling, the ML model 108 transcends the limitations imposed by traditional data processing methodologies, enabling the extraction of actionable insights and intelligence from encrypted data streams with unparalleled accuracy and precision.

[0050]

[0044] . In some aspects of the present disclosure, the ML model 108 embodies a fusion of cutting-edge methodologies and algorithms meticulously tailored to accommodate the unique challenges posed by encrypted data processing. By virtue of its inherent adaptability and scalability, the ML model 108 is adept at traversing diverse data landscapes, encompassing structured, unstructured, and semi-structured data formats. Through iterative learning and refinement, the ML model 108 autonomously refines its predictive capabilities, continually adapting to evolving data patterns and dynamics to deliver insights of the highest caliber.

[0051]

[0045] . In some aspects of the present disclosure, the ML model 108 operates synergistically with the overarching architecture of the system 100, seamlessly interfacing with complementary components such as the data access and encryption module 106 and the federated learning container 112. This cohesive integration fosters a symbiotic relationship wherein the ML model 108 leverages encrypted data processed by the data access and encryption module 106 to generate insights of unprecedented depth and granularity. By harnessing the power of homomorphic encryption and federated learning techniques, the ML model 108 transcends the traditional boundaries of data processing, enabling collaborative analysis and computation across distributed environments while preserving the confidentiality and privacy of sensitive information.

[0052]

[0046] . In some aspects of the present disclosure, the ML model 108 serves as the linchpin of the feedback loop within the system 100, actively soliciting user feedback on the insights generated from encrypted data analyses. By providing users with a platform to evaluate the accuracy, relevance, and efficacy of the generated insights, the ML model 108 fosters a culture of continuous improvement and refinement, driving iterative enhancements to its predictive capabilities. This iterative feedback mechanism empowers users to contribute to the iterative evolution of the ML model 108, ensuring that it remains attuned to the ever-changing needs and preferences of its stakeholders.

[0053]

[0047] . In accordance with the embodiments of the present disclosure, the system 100 may include a feedback mechanism 110 that is configured to enhance the efficacy and adaptability of the system's 100 machine learning (ML) model 108. The feedback mechanism 110 functions as a pivotal component within the system 100, facilitating seamless communication between users and the ML model 108 to solicit valuable insights and assessments regarding the generated insights. Through this feedback loop, users are empowered to provide qualitative or quantitative feedback on the accuracy, relevance, and bias of the insights produced by the ML model 108, thereby contributing to the continuous refinement and optimization of the system's 100 analytical capabilities.

[0054]

[0048] . Upon the generation of insights by the ML model 108, the feedback mechanism 110 initiates the process of soliciting feedback from users and stakeholders within the system. This feedback may encompass a diverse range of perspectives, including subjective assessments of the insights' utility, relevance to specific use cases, and alignment with desired outcomes or objectives. By actively engaging users in the feedback process, the system 100 cultivates a collaborative environment wherein users play an integral role in shaping the evolution and improvement of the analytical framework.

[0055]

[0049] . In some aspects of the present disclosure, the feedback mechanism 110 employs a variety of channels and modalities to solicit feedback from users, catering to diverse preferences and communication styles. These channels may include interactive interfaces, surveys, polls, or direct communication channels, allowing users to provide feedback in a manner that is convenient and accessible. Additionally, the feedback mechanism 110 may leverage advanced analytics and natural language processing techniques to extract actionable insights from user feedback, facilitating the identification of patterns, trends, and areas for improvement within the system 100.

[0056]

[0050] . In some aspects of the present disclosure, the feedback mechanism 110 integrates seamlessly with the ML model 108 to translate user feedback into actionable insights and optimizations. Leveraging sophisticated algorithms and feedback processing pipelines, the feedback mechanism 110 analyzes and synthesizes user feedback to identify opportunities for refinement and enhancement within the ML model 108. This iterative feedback loop fosters a culture of continuous improvement, enabling the system 100 to evolve and adapt in response to changing user needs, preferences, and market dynamics.

[0057]

[0051] . In some aspects of the present disclosure, the feedback mechanism 110 may allow users to provide feedback on the accuracy, relevance, or bias of the insights generated by the ML model 108.

[0058]

[0052] . In accordance with the embodiments of the present disclosure, the system 100 may include a federated learning container 112, meticulously engineered to facilitate efficient and secure computations on encrypted data. The federated learning container 112 represents a sophisticated framework designed to harness the power of federated learning techniques while preserving the confidentiality and privacy of sensitive information. As an integral part of the secure interface system 100, the federated learning container 112 plays a pivotal role in enabling collaborative and distributed data processing across multiple nodes within the Al ecosystem.

[0059]

[0053] . In some aspects of the present disclosure, the federated learning container 112 embodies a decentralized approach to machine learning, wherein computations are performed on encrypted data distributed across disparate nodes or devices. This decentralized paradigm empowers organizations to leverage the collective intelligence of distributed data sources while mitigating privacy risks and regulatory concerns associated with centralized data processing. By partitioning data and computation across multiple nodes, the federated learning container 112 enables organizations to train machine learning models collaboratively without the need to centralize sensitive data in a single location.

[0060]

[0054] . In some aspects of the present disclosure, the functionality of the federated learning container 112 is underpinned by advanced encryption techniques, which ensure that data remains encrypted throughout the computation process. Leveraging state-of-the-art encryption algorithms such as homomorphic encryption, the federated learning container 112 enables computations to be performed directly on encrypted data without the need for decryption. This cryptographic approach preserves the confidentiality and privacy of sensitive information, mitigating the risk of data exposure or unauthorized access during the computation process.

[0055] . In some aspects of the present disclosure, the federated learning container 112 is equipped with robust mechanisms for orchestrating and coordinating distributed computations across multiple nodes within the Al ecosystem. Through sophisticated protocols and communication channels, the federated learning container 112 facilitates seamless collaboration and data exchange between disparate nodes, enabling organizations to harness the collective intelligence of distributed data sources effectively. This collaborative approach enhances the scalability, resilience, and efficiency of machine learning tasks, enabling organizations to tackle complex analytical challenges with ease.

[0061]

[0056] . In some aspects of the present disclosure, the federated learning container 112 incorporates mechanisms for ensuring the integrity and reliability of computation results. By implementing consensus algorithms and validation protocols, the federated learning container 112 safeguards against malicious actors or compromised nodes that may seek to manipulate or tamper with computation outcomes. This robust validation framework enhances the trustworthiness and credibility of computation results, enabling organizations to make informed decisions based on reliable insights derived from encrypted data.

[0062]

[0057] . In some aspects of the present disclosure, the secure interface system

[0063] 100 may facilitates communication between the source 102, user access module 104, data access and encryption module 106, ML model 108, feedback mechanism 110, and federated learning container 112.

[0058] . In some aspects of the present disclosure, the federated learning container 112 may enable distributed processing of the encrypted data without decrypting it.

[0064]

[0059] . Figure 2 illustrates a flowchart that depicts a method 200 of secure interface system 100 for managing access to data, in accordance with an aspect of the present disclosure. The method 200 may include the following steps:

[0065]

[0060] . At step 202, one or more data are received from a source 102.

[0066]

[0061] . At step 204, user access is authenticated to a secure interface system

[0067] 100.

[0068]

[0062] . At step 206, the received data are encrypted using homomorphic encryption.

[0069]

[0063] . At step 208, access to the encrypted data is controlled based on predefined user permissions.

[0070]

[0064] . At step 210, the encrypted data are processed through a federated learning container 112 to generate insights.

[0071]

[0065] . At step 212, feedback is received on the insights generated by the ML model 108.

[0072]

[0066] . At step 214, the feedback is utilized to improve the performance of the

[0073] ML model 108.

[0074]

[0067] . In conclusion, the dynamic optimization system presented herein represents a significant advancement in the field of electric vehicle (EV) infrastructure management. By harnessing real-time data analytics, predictive modelling, and dynamic optimization algorithms, this system offers a comprehensive solution for optimizing the utilization and performance of EV charging stations. Through dynamic resource allocation and scheduling, the system enhances the reliability, scalability, and sustainability of EV charging networks, addressing critical challenges in the transition towards electric mobility.

Claims

Claims:We Claim:

1. A safe humanoid interface system (100) for controlled access to data, comprising: a) A source (102) that is configured to receive one or more data; b) A user access module (104) that is configured to authenticate user access to the system (100); c) A data access and encryption module (106) that is configured to: d) Encrypt data received from the source (102) using homomorphic encryption; and control access to the encrypted data, e) An ML model (108) that is configured to process the encrypted data and generate one or more insights; f) A feedback mechanism (110) that is configured to receive feedback on one or more insights generated by the ML model (108); and g) A federated learning container (112) that is configured to perform computations on the encrypted data; h) Wherein the secure interface system (100) facilitates communication between the source (102), user access module (104), data access and encryption module (106), ML model (108), feedback mechanism (110), and federated learning container (112).

2. The safe humanoid interface system (100) as claimed in claim 1, wherein the user access module (104) utilizes an authentication protocol to verify user identity.

3. The safe humanoid interface system (100) as claimed in claim 1, wherein the data access and encryption module (106) controls access to the encrypted data based on predefined user permissions.

4. The safe humanoid interface system (100) as claimed in claim 1, wherein the feedback mechanism (110) allows users to provide feedback on the accuracy, relevance, or bias of the insights generated by the ML model (108).

5. The safe humanoid interface system (100) as claimed in claim 1, wherein the federated learning container (112) enables distributed processing of the encrypted data without decrypting it.

6. A method (200) of safe humanoid interface system (100) for managing access to data, comprising the steps of: a) Receiving one or more data from a source (102); b) Authenticating user access to a secure interface system (100); c) Encrypting the received data using homomorphic encryption; controlling access to the encrypted data based on predefined user permissions; processing the encrypted data through a federated learning container (112) to generate insights; d) Receiving feedback on the insights generated by the ML model (108); and utilizing the feedback to improve the performance of the ML model (108).

7. The method (200) of safe humanoid interface system (100) as claimed in claim 6, wherein the authentication step utilizes an authentication protocol to verify user identity.

8. The method (200) of safe humanoid interface system (100) as claimed in claim 6, wherein the controlling step involves granting access to specific portions of the encrypted data based on user roles.

9. The method (200) of safe humanoid interface system (100) as claimed in claim 6, wherein the feedback step allows users to provide qualitative or quantitative feedback on the insights.

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